Blockchains and Artificial Intelligence

Blockchains and Artificial Intelligence: Comparison

Please note this is a comparison between Version 1 by Hamed Taherdoost and Version 3 by Conner Chen.

It is undeniable that the adoption of blockchain- and artificial intelligence (AI)-based paradigms is proceeding at lightning speed. Both paradigms provide something new to the market, but the degree of novelty and complexity of each is different. In the age of digital money, blockchains may automate installments to allow for the safe, decentralized exchange of personal data, information, and logs. AI and blockchains are two of the most talked about technologies right now. Using a decentralized, secure, and trustworthy system, blockchain technology can automate bitcoin payments and provide users access to a shared ledger of records, transactions, and data. Through the use of smart contracts, a blockchain may also regulate user interactions without the need for a central authority. As an alternative, AI provides robots with the ability to reason and make decisions and human-level intellect.

blockchain technology
artificial intelligence
AI

1. Introduction

It is not a secret that there are many new technologies being promoted right now, but among them, the blockchain has been gaining a lot of traction as a decentralized ledger system that can be used in a variety of settings ^[1][2][1,2]. Since its inception in the 1920s, blockchain has persisted as a potentially disruptive innovation that will affect the ways of working together, automating payments, monitoring markets, and recording transactions [3]. Blockchain technology has the potential to be very useful in eliminating the need for a central authority figure to oversee and verify transactions and agreements between many parties [4]. Each transaction in the blockchain is cryptographically hashed and verified by all mining nodes [5]. It creates immutable, secure, and accessible timestamped records that can be accessed by all parties involved [6]. Artificial intelligence (AI), which gives machines the ability to learn from data and make decisions based on what they haveve learned, is another very visible area that is gaining a lot of traction. Ongoing statistical polling predicts that by 2030, the AI sector will be worth as much as USD 13 trillion [7].

While numerous competing innovations aim to make information in smart homes impervious to attacks, this is not yet the case. When it comes to protecting the home network from command and control attacks on encrypted data and providing a secure platform for all the devices in the network to connect, the development of blockchain technology is seen as among the most promising [8]. The consensus reached among the nodes in a blockchain ensures that all transactions are recorded permanently [9]. Control attacks on transmitted or stored information are therefore impossible through a single trad. Instead, the preponderance of hubs must be compromised for the attack to succeed [10].

Recent years have seen progress toward the concept of decentralized AI. A combination of these two technologies is the basis for decentralized AI (blockchains and AI) [11]. In a distributed and intermediary-free manner, it allows for the execution and storage of trustworthy, carefully tagged, and shared data on the blockchain [12]. The blockchain is now anticipated as a trusted platform to preserve such information, and simulated intelligence is described to work with massive amounts of data [13]. Blockchains may be programmed with smart contracts, allowing trusted third parties to monitor data access and sharing between users [14]. After being exposed to an autonomous system, a machine, and several scenarios, they may adapt and learn, yielding accurate and reliable decision-making outcomes that are unanimously sanctioned by all blockchain mining nodes ^[15][16][15,16].

As a result, everyone with a vested interest may trust and endorse such decisions. AI procedures using blockchains may provide decentralized deduction of how to promote security and trust in information sharing and choice outcomes across countless independent operators, which can contribute, arrange, and vote on future choices [17].

The convergence of AI and blockchain technology has produced several practical benefits [18]. Blockchain technology allows for the safe keeping of patient records in the healthcare industry. If they are permitted access, medical practitioners may learn valuable lessons from the patterns mined by AI in this data. Remarkably, their combined use has enabled the healthcare sector to deal with the COVID-19 pandemic [19]. One forward-thinking example is BurstIQ, a blockchain-based startup that offers data solutions for the healthcare sector and offers a health wallet using blockchain technology, AI, and big data to handle patient data. Medical providers may access patient health data via the wallet whenever needed [20]. Increased transaction speeds and mutual trust are two ways in which merging these two technologies is revolutionizing the financial services sector [21].

2. Benefits of Blockchains and AI Together

2.1. Automation

AI, automation, and blockchains may provide value to multi-party business processes by decreasing the need for human intervention, boosting throughput, and facilitating better data integrity. AI models incorporated in smart contracts implemented on a blockchain could suggest recalling expired products, reordering, paying, or purchasing stock based on predetermined thresholds and events, resolving disputes, and choosing the most environmentally friendly shipping option, among other things. The goal of the research article by Rajagopal et al. ^[22][35] was to examine the effects of AI and blockchain technology on an automation service. To obtain pertinent data and information on the study subject, the researcher chose a secondary data gathering approach. Similar to this, the resources employed quantitative techniques to make the data easier to interpret. Additionally, this research will aid readers in comprehending the proper efficacy of AI in managing automation activity.

2.2. Augmentation

AI can read, analyze, and correlate data with lightning speed and depth, giving blockchain-based business networks a competitive edge. Blockchain enables AI to expand by enabling access to enormous amounts of data from inside and outside the company, allowing for more actionable insights, better management of data consumption and model sharing, and a more transparent and trustworthy data market. By leveraging third parties, or oracles, to process data, Lopes et al. ^[23][36] suggested an architecture that makes use of blockchain technology as smart contract technology and a ledger for robotic control. They demonstrated how to securely record events, how smart contracts may be used to drive robots, and how to interact with outside AI systems for picture analysis. Since the suggested architecture is simple to integrate, modify, maintain, and expand to other domains, it may be utilized in a variety of settings including manufacturing, network management, and robot control.

2.3. Authenticity

Using the digital record provided by blockchain technology, the AI’s underlying structure and the data source it is drawing from can be better understood, thus overcoming the problem of explainable AI. Trust in data and, by extension, AI-generated suggestions is bolstered as a result. Data security may be improved when a blockchain distributes and stores AI models, particularly when combined with AI. Li et al. ^[24][37] suggested a blockchain-based data security system for AI in 6G networks. After that, they talked about two 6G-related, AI-enabled applications: autonomous vehicles and indoor positioning. They illustrated the efficiency of blockchains in data security via a case study of an indoor navigation system. Blockchain and AI integration is being developed to assess and improve the level of intelligent service.

3. AI and Blockchain Use Cases

Introducing AI into blockchains creates new possibilities in a wide range of industries.

3.1. Supply Chain

AI and blockchains are revolutionizing supply chains across sectors by adding automation and intelligence to perform transactions, making the data trustworthy and shareable, and digitizing a formerly paper-based process. Carbon emissions data can be tracked, for instance, at the product or component level, giving manufacturers more precision and insight into their decarbonization efforts.

3.2. Financial Services

The introduction of trust, the elimination of friction in multi-party transactions, and the acceleration of transaction speeds are just a few ways AI and blockchains are revolutionizing the financial services business. Take the procedure of obtaining a loan for example. Applicants agree to allow blockchain access to their data. Faster closings and higher customer satisfaction may be achieved via a combination of data trust and automated application evaluation procedures.

3.3. Life Sciences

The use of AI and blockchains in the pharmaceutical business has the potential to greatly increase the success rate of clinical trials while also boosting transparency and traceability across the medication supply chain. The integrity of data, automation of trial participation and data collecting, monitoring of patients, management of permission, and openness of data are all made possible by combining powerful data analysis with a decentralized framework for clinical trials.

3.4. Healthcare

AI may assist in improving almost every aspect of healthcare, from bringing to light therapeutic findings and supporting user demands to recognizing insights from uncovering trends and patient data. Blockchain technology allows for the safe sharing of sensitive patient information such as electronic health records (EHR) across healthcare providers.

3.5. Social Network Analysis

Only a relatively small amount of research considered the inherent social networking properties of the blockchain ^[25][38]. In order to forecast personality, multiple works conducted psychological analyses. Based on the findings, various researchers developed various models that enabled recognizing the characteristics of individuals that characterize their personalities. These models make it feasible to understand the connections that underlie personality and psychiatric disorders ^[26][39], work performance and satisfaction ^[27][40], and even interpersonal interactions. Social networks are a good source for personality research of a certain group due to the abundance of information they contain and the millions of members they have. The interactions between blockchain users are really quite important for uncovering previously undiscovered patterns and for opening up fresh perspectives for examining this speculative bubble. To examine the connections in the blockchain network, social network analysis concepts might be of significant assistance. In this exercise, it makes sense to picture a social network where each node represents a user, identified by blockchain address, and where each arc represents a user-to-user transaction. In order to uncover patterns of user behavior throughout a cryptocurrency speculative bubble and gather information about it, Bonifazi et al. ^[25][38] developed a social network analysis-based technique. Their method is all-encompassing and can be used to analyze any cryptocurrency speculative bubble, whether it exists now or in the future. They demonstrated that their technique includes the capacity to help the search for speculators and that this capability can include past, current, and future bubbles.

4. Why Combine AI with Blockchains

The corporate world and bespoke software development services will see significant adoption of these two technologies over the next five to ten years. Industry executives that are both forward-thinking and tech-savvy still see the immense potential of combining blockchains with AI. Let's take a look at how you may use AI and blockchain for your business.

4.1. Understanding How AI Thinks

However advanced AI may be, it will never replace human judgment, and hence will never be widely adopted by the public. The inability to account for the computer's actions is one of the problems that has slowed the widespread use of AI. The public will quickly come to trust AI if its decision-making processes can be recorded.

Incorporating blockchain technology with AI has the potential to reveal previously hidden processes inside computers. Every AI decision may be recorded and made accessible in a distributed ledger. The information on a blockchain cannot be altered once it has been recorded, making it ideal for auditing and other security-sensitive applications.

4.2. Security Improvement

Blockchains include built-in encryption that makes the data very secure. Storing private and sensitive information like medical records or individual recommendations on a blockchain makes a lot of sense. Continuous and massive amounts of data are essential for AI. AI algorithms that can safely process encrypted data are now the focus of intensive research and development.

In any case, there is a supplementary viewpoint regarding the enhancement of security. There is a high level of security in the blockchain itself, but any extra layers or applications are not bulletproof. In the banking sector, machine learning will speed up the rollout of blockchain applications and allow for the prediction of potential system breaches.

4.3. Gaining Entry to and Control Over the Data Market

This is inextricably linked to better safety measures. With the ability to store massive quantities of encrypted data on a distributed ledger and have AI efficiently manage it, fresh use cases emerge. Blockchain technology makes it possible to keep sensitive information, such as medical records, and even benefit from providing others with access to it. That's why there are now markets for data, models, and AI.

Enhancing the data-management processes is another advantage of integrating AI with the blockchain. To decipher encrypted data, computers go through possible character permutations until they find the one that matches the original message. A hacking AI is like a person in that it improves with practice. AI, however, will not need a human lifetime to achieve the same level of expertise. This may be accomplished fairly rapidly with sufficient training data.

4.4. Smart Contracts Enhancement

Certain vulnerabilities in the blockchain’'s underlying technology provide a risk for malicious actors. This was very recently shown. To put it another way,: smart contracts are not that smart y. Yet. Once certain triggers are reached, they will automatically release and transfer the monies. This can only be accomplished after the blockchain community has reached a unified decision. Since the code for a smart contract is openly available, anybody may take their time to carefully examine it for vulnerabilities. The use of AI aids in the validation of smart contracts and the forecasting of exploitable flaws. Li et al. ^[28][41] created Astraea, a private smart contract-based, secure, anonymous, and decentralized auditing platform for contribution systems. In particular, they combined a Distribute Smart Contract (DiSC) with an SGX Enclave to distribute contributions, demonstrate the accuracy of the gift number (intention), and protect the anonymity of donors. They created a donation smart contract by using a DiSC to reimburse deposits and protect against theft and collusion attacks from nefarious collectors and transponders. They used security reduction to explicitly describe and demonstrate Astraea’'s security and privacy. To carry out an in-depth performance study, they constructed a prototype of Astraea. Astraea is efficient in terms of both computing and communication, according to experimental data.

4.5. Maximizing Energy Efficiency

Energy consumption is high while data mining. This is a huge problem in the contemporary world, but Google has shown that machine learning can solve it. By feeding the DeepMind AI historical data from hundreds of sensors inside a data center, Google has enabled to cut down on the amount of energy needed to keep its data center at a comfortable temperature by 40 percent. Using this similar concept, mining hardware costs may be reduced.

5. Challenges

What are the challenges with combining blockchain with AI?

5.1. Privacy and Security

Among the obstacles of blockchain application, privacy, security, and landing protection are major concerns ^[29][76]. Due to its role as the backbone of the Internet of Value, the blockchain's inter-node communications are public and transparent, but they may also include sensitive data that users would like to keep secret. So, the key to whether or not blockchain applications can be deployed on a big scale is how to safeguard user privacy. Typical blockchain privacy protection strategies include information concealing and identity confusion. Using privacy-protecting signature technologies like ring signatures and group signatures to muddle the identities of both participants in a transaction, identity obfuscation technology makes it hard to match the true user to their blockchain transaction. The supervisor's private key allows the supervisor to access user data as required, protecting users' identities.

The user's transaction privacy is successfully protected by information concealing, which employs technologies like secure multiparty computing and zero-knowledge proof to complete transactions without disclosing any private information and to guarantee the credibility of the findings. The increased complexity of the calculations, however, results in a less effective system, therefore more work has to be done to boost its usefulness in real-world contexts. It's not easy to figure out how to apply AI algorithms sensibly to boost inefficient performance. Furthermore, the current AI algorithm has to be redesigned to be applied to a distributed context.

Private AI, which combines AI and encryption methods to solve the data security problem, was recently developed, buthowever prior research has demonstrated that model inversion attacks may be used to reverse-engineer the model parameters to create pictures ^[30][77]. In this context, Khowaja et al. ^[31][78] suggested an industrial IoT environment-specific federated learning and encryption-based private (FLEP) AI system that offers two-tier security for data and model parameters. They provided a hypothetical approach to protect the model parameters together with a three-layer encryption mechanism for data security. The suggested approach, according to experimental data, produces improved encryption quality at the cost of a somewhat longer execution time. By applying a trust-based protection mechanism, Corradini et al. ^[30][77] suggested a two-tier blockchain architecture to improve the security and independence of smart items in the IoT. Smart items are appropriately categorized into communities in this architecture. The first-tier blockchain is local and is only used to record probing transactions carried out to assess the confidence of an item in another one of athe different community or of a same community, which reduces the complexity of the solution. These transactions are periodically aggregated after a time interval, and the resulting values are kept on the secondary blockchain. In pParticular, the ly, stored values are each object’'s standing within its community and each community’'s confidence in the other communities inside the framework.

Moreover, the blockchain and fFederated lLearning integration method has drawn a lot of interest as a new trustworthy data- sharing pattern with privacy protection. Generally speaking, this approach bypasses the supervision of the computing process and fFederated lLearning model in favor of using blockchain technology to oversee the original data and computation outcomes [14]. In order to create a new data privacy sharing paradigm using bBlockchains and f and Federated lLearning, Guo et al. [14] presented the ideas of the sandbox and state channel. They primarily addressed issues with data privacy sharing in federated learning and the deterioration of system performance brought on by poor data quality. The simulation results demonstrate that the suggested strategy outperforms and is more effective than the conventional data exchange method.

5.2. Credible Oracles

Blockchain players may trigger the execution of a smart contract by triggering an external event or calling a third-party function. Event or data retrieval automation is not a primary focus of smart contracts' design. To rephrase, the contracts are unable to get information from the real world. The contracts need to be "pushed" data and events. To address these issues, it is recommended to employ trustworthy oracles, which are essentially trusted external parties or nodes, to transmit events and data to smart contracts. When it comes to maintaining trust, oracles provide a new layer of complexity and potential security risks, as a previously decentralized system becomes centered on a set of oracles that must be relied upon. Usually, the agreement is reached by a vote amongst reliable oracles ^[32][79].

5.3. Concerning the Security of Smart Contracts and the Implications of Their Deterministic Execution

The success of a smart contract relies on its implementation being safe against hacks and errors. Code and data on the network should be protected against intrusion wherever possible. For instance, in 2016, hackers exploited a critical flaw in the coding of the Ethereum platform used to create the smart contract for the DAO. There was a loss of 3.6 million Ethers as a consequence of this. This problem, introduced by smart contract programming and other blockchain-based applications, calls for blockchain engineering ^[33][80]. Problems with security in smart contracts may be traced back to careless coding in the languages used to create them. The relevance of vulnerability testing for smart contracts has grown, and as a result, several tools have been created to evaluate the safety of a contract’'s source code ^[34][35][81, 82]. Moreover, as it stands right now, there is no such thing as a probabilistic result for the execution of a smart contract. When AI and machine learning-based decision-making algorithms are implemented as smart contracts by the mining nodes, the execution output is typically not deterministic but rather random, unpredictable, and approximative ^[36][83]. This may be a significant difficulty for decentralized AI. With data input that might be rapidly changing as much as that of IoT and sensory readings, this calls for a unique approach to deal with approximation computation and to design consensus protocols for mining nodes for agreeing on outputs with a certain degree of confidence, accuracy, or precision.

5.4. Scalability

The key to the successful rollout of smart blockchain applications is in solving the scalability problem ^[37][84]. Blockchain decentralized applications need the underlying blockchain platform to function. If the scalability and performance of the system are inadequate, it cannot be deployed as a large-scale application. The bBlockchain’'s scaling concerns may be broken down into three primary categories: consistency problems, network latency, and performance constraints. Most nodes need to agree on the transaction data to guarantee the blockchain’'s security. The blockchain will split if the need for consistency in the distributed network is neglected in favor of faster growth. Due to its decentralized nature, blockchain’'s scalability is limited by the time it takes for data to travel between nodes in the network. This is particularly true for longer delays. The key problem that prevents the widespread the use of blockchain applications is the impact of transaction performance on scalability ^[38][85]. To maintain security and ultimate consistency, blockchain transactions cannot be completed in parallel, which makes it impossible to boost transaction throughput.

5.5. Off-Chain and On-Chain Storage Data Cooperation

Blockchain technologies and conventional information storage methods both have advantages and disadvantages. Both conventional information systems and blockchains require off-chain storage and compute infrastructure to boost performance. To accomplishdo this, it is necessaryeeded to combine blockchain technology with conventional information systems, with the most important consideration being to guarantee the accuracy and consistency of both the data on the chain and the data that areis stored in conventional databases. More importantly, data areis essential to the advancement of AI. There are still several obstacles to the widespread use of AI, such as issues with data quality, data monopolization, and ddata abuse, and so on. The introduction of blockchain technology opens up new avenues for solving these issues. The marriage of blockchains and AI is only useful in the real economy if the data on the chain areis properly combined with the data off the chain ^[39][86].

In order to enable model sharing and ensure a fair model-money exchanging process between independent developers and ML-as-a-service (MLaaS) providers, Weng et al. ^[40][87] developed a model marketplace dubbed Golden Grain. To encourage the loyal contributions of well-trained models, they implemented the swapping process on the blockchain and subsequently created a blockchain-enabled model benchmarking procedure for openly deciding the model values in accordance with their real-world performances. Their marketplace carefully offloads the laborious computation and designs a protected off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for guaranteeing both the integrity and authenticity of benchmarking, in particularly to reduce the blockchain overhead for model benchmarking. In order to show the realistically inexpensive performance of their architecture, they deployed a prototype of the Golden Grain on the Ethereum blockchain and carry out comprehensive testing using common benchmark datasets.